Remanufacturing system scheduling is an essential and effective approach to realize the digitization and greening of the remanufacturing industry. However, previous researches on the remanufacturing system scheduling problem mainly consider a single or two production stages and economic objectives. In this paper, by integrating the three core production stages, i.e., disassembly, reprocessing and reassembly together, we study the energy-aware remanufacturing system scheduling problem in which the well-accepted Turn Off and On strategy is also considered. First, a mathematical model aiming at minimizing the total energy consumption (TEC) of the remanufacturing system is established. Then, a hybrid genetic algorithm based on variable neighborhood search (GAVNS) solution method is proposed, given the NP-hard nature of the problem. In GAVNS, each chromosome is encoded by a job sequence and three different decoding methods are specially designed according to the formation of optimization objective TEC. To enhance the algorithm's local search capability, the variable neighborhood search technique is introduced. The feasibility and effectiveness of GAVNS in addressing the energy-aware remanufacturing system scheduling problem is verified through simulation experiments on a set of designed test instances. Experimental results also demonstrate that: (1) the Turn Off and On strategy can effectively reduce TEC of the remanufacturing system, which can reach an energy saving rate of 6.68%; (2) the performance of those decoding methods varies with respect to the problem size; (3) the decoding method based on minimizing the energy consumption of the remanufacturing system (namely DM3) has the best performance among the three decoding methods in most cases; (4) GAVNS is more effective than its four peers, i.e., a variant GAVNS_R, iterated greedy algorithm (IG), extended artificial bee colony algorithm (EABC), discrete invasive weed optimization algorithm (DIWO) in seeking the optimal schedule.

再制造系统调度是实现再制造行业数字化、绿色化必不可少的有效途径。然而，以往对再制造系统调度问题的研究主要考虑单个或两个生产阶段和经济目标。在本文中，通过将拆卸、再加工和重组三个核心生产阶段整合在一起，我们研究了能量感知再制造系统调度问题，其中还考虑了广为接受的关闭和开启策略。首先，一个旨在最小化总能量消耗的数学模型（TEC)再制造体系建立。然后，鉴于问题的 NP 难性质，提出了一种基于变邻域搜索 (GAVNS) 求解方法的混合遗传算法。在GAVNS中，每条染色体由一个作业序列编码，并根据优化目标TEC的形成专门设计了三种不同的解码方法。为了增强算法的局部搜索能力，引入了变邻域搜索技术。通过在一组设计的测试实例上进行仿真实验，验证了 GAVNS 在解决能量感知再制造系统调度问题方面的可行性和有效性。实验结果也表明：（1）Turn Off and On策略可以有效降低TEC再制造系统，可达到6.68%的节能率；(2) 这些解码方法的性能因问题大小而异；(3) 基于最小化再制造系统能耗的译码方法(即DM3)在大多数情况下是三种译码方法中性能最好的；(4) GAVNS 比它的四个同行，即变体GAVNS_R、迭代贪婪算法(IG)、扩展人工蜂群算法(EABC)、离散入侵杂草优化算法(DIWO)更有效地寻求最优调度。